Visualization Technology and Deep-Learning for Multilingual Spam Message Detection

Abstract

Spam detection is an essential and unavoidable problem in today’s society. Most of the existing studies have used string-based detection methods with models and have been conducted on a single language, especially with English datasets. However, in the current global society, research on languages other than English is needed. String-based spam detection methods perform different preprocessing steps depending on language type due to differences in grammatical characteristics. Therefore, our study proposes a text-processing method and a string-imaging method. The CNN 2D visualization technology used in this paper can be applied to datasets of various languages by processing the data as images, so they can be equally applied to languages other than English. In this study, English and Korean spam data were used. As a result of this study, the string-based detection models of RNN, LSTM, and CNN 1D showed average accuracies of 0.9871, 0.9906, and 0.9912, respectively. On the other hand, the CNN 2D image-based detection model was confirmed to have an average accuracy of 0.9957. Through this study, we present a solution that shows that image-based processing is more effective than string-based processing for string data and that multilingual processing is possible based on the CNN 2D model.

Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection

“…Therefore, we have presented a more accurate approach to detecting SMS spam messages. Other papers dealt with SMS Spam messages using local databases in different languages, such as [28] or databases containing spam images and texts like [29].…”